In more and more applications “real world” sensor data is used for example, for analytics functions and situational awareness issues. Sensor selection in wireless networks or in participatory sensing scenarios with mobile sensor nodes is often associated with challenges in selecting a subset of relevant sensor nodes at a specific time in order to save energy and communication bandwidth. In participatory sensing the sensor selection problem can be similar to that of wireless sensor networks in general. Additionally, participatory sensing may consider human mobility, which is not directly controllable.
Some approaches for temporal selection use prediction models in which a sensor value is only sent if it deviates from the predicted value more than a given threshold. Other approaches set sampling frequencies of nodes in order to save energy by setting fixed sampling frequencies (duty cycles) on all deployed sensor nodes and coordinating the nodes in order to minimize the delay of an event detection. For spatial selection, sensor nodes may be selected to cover a region of interest in a grid structure. Other approaches select sensor nodes both spatially and temporally based on statistical models about an observed phenomenon.
One issue in the above mentioned approaches is that selecting a minimal subset of nodes to cover the whole deployment region and only change this subset after a predefined time period to avoid energy depletion does not scale well with mobile nodes as the minimal coverage subset changes constantly. In addition, coverage-based selection is not practical for sensors and applications in which a natural coverage range for sensor cannot be specified (e.g. accurate measurements of air pollutants).
An issue of coverage oriented spatial selection is that it is hard to apply to sensors for which a coverage area is not naturally given. For instance a carbon-dioxide sensor provides a point measurement with a zero coverage radius. Setting an artificial coverage radius would therefore introduce further uncertainty that is counterproductive for accurately reconstructing the observed phenomenon.
It is therefore a need to improve spatial and temporal selection of sensor nodes.